Much has been written about Data Scientist as the sexiest job of the 21st century, but this is only half of the story. Data science as a growing discipline and competitive advantage isn’t possible without a modernized, secure architecture in which to capture, store, organize, transport and protect the big data used by Data scientists and business executives. This behind-the-scenes database and cloud infrastructure is the responsibility of Big Data engineers (or Data engineers), and many companies have realized that these hires come first as they cultivate Data science maturity.
In the Internet of Things, immense streams of big data from endless sources are produced at an unprecedented speed every day. Due to the size and ubiquity of this "big" data, traditional methods of storing and processing that data have fallen short, now relying on more powerful databases and modern, secure cloud-based storage services like AWS and Azure. Optimizing this data for business and operational use requires specialized data engineering expertise to build, maintain, and manage the big data environment. This enabling tech ecosystem and its related governance, processes and practices are often referred to as part of DevOps (or MLOps for machine learning applications.
The hire and support of Big Data engineers is essential to building an organization's foundational big data and data science competencies. In this guide, we’ll explain how engineering and data science connects to big data, the roles and responsibilities of a Big Data engineer, and how you can prepare for a career in this exciting, growing field.
What is Big Data Engineering?
Big Data engineering is the practice of using ever-evolving tech solutions and platforms to manage the capture and ingestion, secure storage, transport, integration and use of big data within an organization. Building and maintaining massive data processing systems, powerful databases and cloud-based services in large-scale computing environments are part of Big Data engineering.
What is a Big Data Engineer?
A Big Data engineer is primarily responsible for the end-to-end collection, organization and structure, and management of big data, using their knowledge, software development and programming skills and other tech competencies to develop enterprise solutions, whether software systems and APIs, databases, cloud services, tools and frameworks, or the integration of all these solutions.
Depending on an organization’s level of data science maturity, sometimes a (Big) Data Engineer’s role will extend into the analysis and visualization of big data, typically the domain and responsibility of Data scientists. This also goes the other way around, with Data scientists occasionally functioning as Big Data engineers.
A Lead Big Data Engineer will typically have additional responsibilities and skills, including the leadership and mentorship of other data engineers and/or data scientists, a higher level of business acumen (e.g., the economics of energy, HealthTech or FinTech), and the ability to contribute to (or lead) data acquisition strategy through partnerships or other means.
What Does a Big Data Engineer Do?
A Data engineer, in general, is responsible for developing the integrated big data systems and architecture that allow Data scientists to structure and transform big data into strategic insights and recommendations. The responsibilities of a Big Data engineer can vary but most likely include:
- Collaborate with other software engineers, data scientists, data architects, IT or DevOps teams, and business managers or executives to establish objectives, execute projects (often working in Agile or Scrum) and deliver against key outcomes
- Build and maintain data management systems and solutions to meet specific requirements, including security and scalability
- Develop computer programs to audit and structure big data at scale
- Seek new opportunities to acquire, clean and improve the use of big data, constantly seeking out new tech solutions or business ideas
What Skills Do You Need to Be a Big Data Engineer?
Big Data engineers need a background in software engineering and programming. Necessary Big Data engineer skills include:
- Business Acumen: The end goal for most Big Data engineers is to improve profits and processes for an organization. An understanding of basic business principles is important for all aspects of Big Data engineering, from developing project goals to communicating with the executive team.
- Cloud Knowledge: Cloud storage and processing is a preferred tool of Big Data engineers. It exceeds hard-drive servers in distributed access and scalability.
- Database Knowledge: The structure and language of databases are important skills for Big Data engineers. Data storage, organization, and searching are key aspects of a Big Data engineering job, and databases are the core element for those operations.
- Data Warehouse Knowledge: Big Data engineers must be skilled in structured query language (SQL) and NoSQL-based data warehousing structures and languages. Other important data warehouse knowledge includes object database, document store, native multi-model database, and key-value cache.
- Machine Learning: For sorting and processing large amounts of data in a short time, machine learning is essential. Machine learning algorithms learn by processing data sets, so machine learning and big data are inextricably linked.
- Programming: Java and Python are the two most used programming languages for Big Data engineers. They should also be proficient in Apache Kafka and Scala.
- Statistics: This is a primary skill for Data scientists who work with Big Data engineers. Data engineers should understand the basics of statistics to communicate effectively with the Data scientists and lead the team.
Big Data Engineer vs. Data Scientist
At a larger corporation or a data science "mature" organization, while they have an overlapping skill set and will collaborate on initiatives, a Big Data engineer and a Data scientist are distinctly different positions. A Big Data engineer develops and maintains the increasingly cloud-based architecture that captures, organizes and secures big data. A Data scientist analyzes that data at scale to answer big questions, make better recommendations, and predict future outcomes.
At a smaller company or company earlier on in its data science journey, the Data engineer and Data science roles can be more blended, with hires wearing more hats. For some, this is an exciting opportunity to gain practical, real-world experience and skills that will make them more well-rounded professionals. Other professionals may prefer greater structure, clarity and delineation of these roles.
What is the Salary of a Big Data Engineer?
The average salary for a Big Data engineer in the U.S. is $123,408. Potential earnings for experienced Big Data engineers in the U.S. range as high as $307,000 including bonuses and profit sharing where applicable.
How Do You Become a Data Engineer?
The preparation for a Big Data engineering position begins with a solid foundation in computer science. You will also need some work experience in IT where you can practice and expand your analytical, logic, and problem-solving skills. A Master’s Degree in Computer Science from MCS@Rice can propel you into a career as a Big Data engineer.